Overview

Brought to you by YData

Dataset statistics

Number of variables17
Number of observations2111
Missing cells0
Missing cells (%)0.0%
Duplicate rows9
Duplicate rows (%)0.4%
Total size in memory280.5 KiB
Average record size in memory136.1 B

Variable types

Numeric8
Categorical5
Boolean4

Alerts

Dataset has 9 (0.4%) duplicate rowsDuplicates
Gender is highly overall correlated with Height and 1 other fieldsHigh correlation
Height is highly overall correlated with GenderHigh correlation
NObeyesdad is highly overall correlated with Gender and 2 other fieldsHigh correlation
Weight is highly overall correlated with NObeyesdad and 1 other fieldsHigh correlation
family_history_with_overweight is highly overall correlated with NObeyesdad and 1 other fieldsHigh correlation
SCC is highly imbalanced (73.3%)Imbalance
SMOKE is highly imbalanced (85.4%)Imbalance
CAEC is highly imbalanced (58.1%)Imbalance
MTRANS is highly imbalanced (57.1%)Imbalance
FAF has 411 (19.5%) zerosZeros
TUE has 557 (26.4%) zerosZeros

Reproduction

Analysis started2024-08-29 03:32:09.058148
Analysis finished2024-08-29 03:32:21.012696
Duration11.95 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

Age
Real number (ℝ)

Distinct1402
Distinct (%)66.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.3126
Minimum14
Maximum61
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.6 KiB
2024-08-29T00:32:21.156170image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile17.891428
Q119.947192
median22.77789
Q326
95-th percentile38.09807
Maximum61
Range47
Interquartile range (IQR)6.052808

Descriptive statistics

Standard deviation6.3459683
Coefficient of variation (CV)0.26101562
Kurtosis2.826389
Mean24.3126
Median Absolute Deviation (MAD)3.22211
Skewness1.5291004
Sum51323.898
Variance40.271313
MonotonicityNot monotonic
2024-08-29T00:32:21.349811image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18 128
 
6.1%
26 101
 
4.8%
21 96
 
4.5%
23 89
 
4.2%
19 59
 
2.8%
20 48
 
2.3%
22 39
 
1.8%
17 30
 
1.4%
24 18
 
0.9%
25 16
 
0.8%
Other values (1392) 1487
70.4%
ValueCountFrequency (%)
14 1
 
< 0.1%
15 1
 
< 0.1%
16 9
0.4%
16.093234 1
 
< 0.1%
16.129279 1
 
< 0.1%
16.172992 1
 
< 0.1%
16.198153 1
 
< 0.1%
16.240576 1
 
< 0.1%
16.270434 1
 
< 0.1%
16.30687 2
 
0.1%
ValueCountFrequency (%)
61 1
< 0.1%
56 1
< 0.1%
55.24625 1
< 0.1%
55.137881 1
< 0.1%
55.022494 1
< 0.1%
55 2
0.1%
52 1
< 0.1%
51 1
< 0.1%
50.832559 1
< 0.1%
47.7061 1
< 0.1%

Gender
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.6 KiB
Male
1068 
Female
1043 

Length

Max length6
Median length4
Mean length4.9881573
Min length4

Characters and Unicode

Total characters10530
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowFemale
3rd rowMale
4th rowMale
5th rowMale

Common Values

ValueCountFrequency (%)
Male 1068
50.6%
Female 1043
49.4%

Length

2024-08-29T00:32:21.539438image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-29T00:32:21.708999image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
male 1068
50.6%
female 1043
49.4%

Most occurring characters

ValueCountFrequency (%)
e 3154
30.0%
a 2111
20.0%
l 2111
20.0%
M 1068
 
10.1%
F 1043
 
9.9%
m 1043
 
9.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10530
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 3154
30.0%
a 2111
20.0%
l 2111
20.0%
M 1068
 
10.1%
F 1043
 
9.9%
m 1043
 
9.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10530
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 3154
30.0%
a 2111
20.0%
l 2111
20.0%
M 1068
 
10.1%
F 1043
 
9.9%
m 1043
 
9.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10530
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 3154
30.0%
a 2111
20.0%
l 2111
20.0%
M 1068
 
10.1%
F 1043
 
9.9%
m 1043
 
9.9%

Height
Real number (ℝ)

HIGH CORRELATION 

Distinct1574
Distinct (%)74.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7016774
Minimum1.45
Maximum1.98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.6 KiB
2024-08-29T00:32:21.877558image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.45
5-th percentile1.5482905
Q11.63
median1.700499
Q31.768464
95-th percentile1.85
Maximum1.98
Range0.53
Interquartile range (IQR)0.138464

Descriptive statistics

Standard deviation0.09330482
Coefficient of variation (CV)0.054831088
Kurtosis-0.56294889
Mean1.7016774
Median Absolute Deviation (MAD)0.069769
Skewness-0.012854646
Sum3592.2409
Variance0.0087057894
MonotonicityNot monotonic
2024-08-29T00:32:22.093271image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.7 60
 
2.8%
1.65 50
 
2.4%
1.6 43
 
2.0%
1.75 39
 
1.8%
1.62 36
 
1.7%
1.8 28
 
1.3%
1.72 19
 
0.9%
1.63 17
 
0.8%
1.67 16
 
0.8%
1.78 15
 
0.7%
Other values (1564) 1788
84.7%
ValueCountFrequency (%)
1.45 1
 
< 0.1%
1.456346 1
 
< 0.1%
1.48 1
 
< 0.1%
1.481682 1
 
< 0.1%
1.483284 1
 
< 0.1%
1.486484 1
 
< 0.1%
1.489409 1
 
< 0.1%
1.491441 1
 
< 0.1%
1.498561 1
 
< 0.1%
1.5 13
0.6%
ValueCountFrequency (%)
1.98 1
< 0.1%
1.975663 1
< 0.1%
1.947406 1
< 0.1%
1.942725 1
< 0.1%
1.931263 1
< 0.1%
1.930416 1
< 0.1%
1.93 2
0.1%
1.92 1
< 0.1%
1.919543 1
< 0.1%
1.918859 1
< 0.1%

Weight
Real number (ℝ)

HIGH CORRELATION 

Distinct1525
Distinct (%)72.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean86.586058
Minimum39
Maximum173
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.6 KiB
2024-08-29T00:32:22.297947image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum39
5-th percentile48.5
Q165.473343
median83
Q3107.43068
95-th percentile131.91615
Maximum173
Range134
Interquartile range (IQR)41.957339

Descriptive statistics

Standard deviation26.191172
Coefficient of variation (CV)0.30248717
Kurtosis-0.69989816
Mean86.586058
Median Absolute Deviation (MAD)21.735215
Skewness0.2554105
Sum182783.17
Variance685.97748
MonotonicityNot monotonic
2024-08-29T00:32:22.504631image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80 59
 
2.8%
70 43
 
2.0%
50 42
 
2.0%
75 40
 
1.9%
60 37
 
1.8%
65 26
 
1.2%
42 22
 
1.0%
90 20
 
0.9%
78 19
 
0.9%
45 18
 
0.9%
Other values (1515) 1785
84.6%
ValueCountFrequency (%)
39 1
< 0.1%
39.101805 1
< 0.1%
39.371523 1
< 0.1%
39.695295 1
< 0.1%
39.850137 1
< 0.1%
40 1
< 0.1%
40.202773 1
< 0.1%
40.343463 1
< 0.1%
41.220175 1
< 0.1%
41.268597 1
< 0.1%
ValueCountFrequency (%)
173 1
< 0.1%
165.057269 1
< 0.1%
160.935351 1
< 0.1%
160.639405 1
< 0.1%
155.872093 1
< 0.1%
155.242672 1
< 0.1%
154.618446 1
< 0.1%
153.959945 1
< 0.1%
153.149491 1
< 0.1%
152.720545 1
< 0.1%

CALC
Categorical

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size16.6 KiB
Sometimes
1401 
no
639 
Frequently
 
70
Always
 
1

Length

Max length10
Median length9
Mean length6.9128375
Min length2

Characters and Unicode

Total characters14593
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowno
2nd rowSometimes
3rd rowFrequently
4th rowFrequently
5th rowSometimes

Common Values

ValueCountFrequency (%)
Sometimes 1401
66.4%
no 639
30.3%
Frequently 70
 
3.3%
Always 1
 
< 0.1%

Length

2024-08-29T00:32:22.710312image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-29T00:32:22.883885image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
sometimes 1401
66.4%
no 639
30.3%
frequently 70
 
3.3%
always 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 2942
20.2%
m 2802
19.2%
o 2040
14.0%
t 1471
10.1%
s 1402
9.6%
S 1401
9.6%
i 1401
9.6%
n 709
 
4.9%
y 71
 
0.5%
l 71
 
0.5%
Other values (7) 283
 
1.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14593
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 2942
20.2%
m 2802
19.2%
o 2040
14.0%
t 1471
10.1%
s 1402
9.6%
S 1401
9.6%
i 1401
9.6%
n 709
 
4.9%
y 71
 
0.5%
l 71
 
0.5%
Other values (7) 283
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14593
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 2942
20.2%
m 2802
19.2%
o 2040
14.0%
t 1471
10.1%
s 1402
9.6%
S 1401
9.6%
i 1401
9.6%
n 709
 
4.9%
y 71
 
0.5%
l 71
 
0.5%
Other values (7) 283
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14593
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 2942
20.2%
m 2802
19.2%
o 2040
14.0%
t 1471
10.1%
s 1402
9.6%
S 1401
9.6%
i 1401
9.6%
n 709
 
4.9%
y 71
 
0.5%
l 71
 
0.5%
Other values (7) 283
 
1.9%

FAVC
Boolean

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
True
1866 
False
245 
ValueCountFrequency (%)
True 1866
88.4%
False 245
 
11.6%
2024-08-29T00:32:23.032377image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

FCVC
Real number (ℝ)

Distinct810
Distinct (%)38.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4190431
Minimum1
Maximum3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.6 KiB
2024-08-29T00:32:23.361465image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.5232145
Q12
median2.385502
Q33
95-th percentile3
Maximum3
Range2
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.53392658
Coefficient of variation (CV)0.2207181
Kurtosis-0.6375459
Mean2.4190431
Median Absolute Deviation (MAD)0.385502
Skewness-0.43290583
Sum5106.5999
Variance0.28507759
MonotonicityNot monotonic
2024-08-29T00:32:23.581192image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 652
30.9%
2 600
28.4%
1 33
 
1.6%
2.823179 2
 
0.1%
2.21498 2
 
0.1%
2.795086 2
 
0.1%
2.442536 2
 
0.1%
2.81646 2
 
0.1%
2.938031 2
 
0.1%
2.954996 2
 
0.1%
Other values (800) 812
38.5%
ValueCountFrequency (%)
1 33
1.6%
1.003566 1
 
< 0.1%
1.005578 1
 
< 0.1%
1.00876 1
 
< 0.1%
1.031149 1
 
< 0.1%
1.036159 1
 
< 0.1%
1.036414 1
 
< 0.1%
1.052699 1
 
< 0.1%
1.053534 1
 
< 0.1%
1.063449 1
 
< 0.1%
ValueCountFrequency (%)
3 652
30.9%
2.998441 1
 
< 0.1%
2.997951 1
 
< 0.1%
2.997524 1
 
< 0.1%
2.996717 1
 
< 0.1%
2.996186 1
 
< 0.1%
2.995599 1
 
< 0.1%
2.99448 1
 
< 0.1%
2.992329 1
 
< 0.1%
2.992205 1
 
< 0.1%

NCP
Real number (ℝ)

Distinct635
Distinct (%)30.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.685628
Minimum1
Maximum4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.6 KiB
2024-08-29T00:32:23.785870image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12.658738
median3
Q33
95-th percentile3.750881
Maximum4
Range3
Interquartile range (IQR)0.341262

Descriptive statistics

Standard deviation0.77803865
Coefficient of variation (CV)0.28970454
Kurtosis0.38552662
Mean2.685628
Median Absolute Deviation (MAD)0
Skewness-1.1070973
Sum5669.3608
Variance0.60534414
MonotonicityNot monotonic
2024-08-29T00:32:23.987536image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 1203
57.0%
1 199
 
9.4%
4 69
 
3.3%
2.77684 2
 
0.1%
3.985442 2
 
0.1%
1.73762 2
 
0.1%
1.894384 2
 
0.1%
1.104642 2
 
0.1%
2.644692 2
 
0.1%
3.559841 2
 
0.1%
Other values (625) 626
29.7%
ValueCountFrequency (%)
1 199
9.4%
1.000283 1
 
< 0.1%
1.000414 1
 
< 0.1%
1.00061 1
 
< 0.1%
1.001383 1
 
< 0.1%
1.001542 1
 
< 0.1%
1.001633 1
 
< 0.1%
1.005391 1
 
< 0.1%
1.009426 1
 
< 0.1%
1.010319 1
 
< 0.1%
ValueCountFrequency (%)
4 69
3.3%
3.999591 1
 
< 0.1%
3.998766 1
 
< 0.1%
3.998618 1
 
< 0.1%
3.995957 1
 
< 0.1%
3.995147 1
 
< 0.1%
3.994588 1
 
< 0.1%
3.990925 1
 
< 0.1%
3.98955 1
 
< 0.1%
3.989492 1
 
< 0.1%

SCC
Boolean

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
False
2015 
True
 
96
ValueCountFrequency (%)
False 2015
95.5%
True 96
 
4.5%
2024-08-29T00:32:24.157097image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

SMOKE
Boolean

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
False
2067 
True
 
44
ValueCountFrequency (%)
False 2067
97.9%
True 44
 
2.1%
2024-08-29T00:32:24.292545image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

CH2O
Real number (ℝ)

Distinct1268
Distinct (%)60.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0080114
Minimum1
Maximum3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.6 KiB
2024-08-29T00:32:24.462107image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11.5848125
median2
Q32.47742
95-th percentile3
Maximum3
Range2
Interquartile range (IQR)0.8926075

Descriptive statistics

Standard deviation0.61295345
Coefficient of variation (CV)0.30525397
Kurtosis-0.87939461
Mean2.0080114
Median Absolute Deviation (MAD)0.452986
Skewness-0.10491164
Sum4238.9121
Variance0.37571193
MonotonicityNot monotonic
2024-08-29T00:32:24.672803image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 448
 
21.2%
1 211
 
10.0%
3 162
 
7.7%
2.825629 3
 
0.1%
1.636326 3
 
0.1%
2.115967 2
 
0.1%
2.174248 2
 
0.1%
2.530035 2
 
0.1%
2.450069 2
 
0.1%
1.439962 2
 
0.1%
Other values (1258) 1274
60.4%
ValueCountFrequency (%)
1 211
10.0%
1.000463 1
 
< 0.1%
1.000536 1
 
< 0.1%
1.000544 1
 
< 0.1%
1.000695 1
 
< 0.1%
1.001307 1
 
< 0.1%
1.001995 1
 
< 0.1%
1.002292 1
 
< 0.1%
1.003063 1
 
< 0.1%
1.003563 1
 
< 0.1%
ValueCountFrequency (%)
3 162
7.7%
2.999495 1
 
< 0.1%
2.994515 1
 
< 0.1%
2.993448 1
 
< 0.1%
2.991671 1
 
< 0.1%
2.989389 1
 
< 0.1%
2.988771 1
 
< 0.1%
2.987718 1
 
< 0.1%
2.987406 1
 
< 0.1%
2.984323 1
 
< 0.1%

family_history_with_overweight
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
True
1726 
False
385 
ValueCountFrequency (%)
True 1726
81.8%
False 385
 
18.2%
2024-08-29T00:32:24.848385image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

FAF
Real number (ℝ)

ZEROS 

Distinct1190
Distinct (%)56.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0102977
Minimum0
Maximum3
Zeros411
Zeros (%)19.5%
Negative0
Negative (%)0.0%
Memory size16.6 KiB
2024-08-29T00:32:25.011927image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.124505
median1
Q31.6666775
95-th percentile2.677133
Maximum3
Range3
Interquartile range (IQR)1.5421725

Descriptive statistics

Standard deviation0.85059243
Coefficient of variation (CV)0.84192257
Kurtosis-0.62058776
Mean1.0102977
Median Absolute Deviation (MAD)0.804157
Skewness0.49848961
Sum2132.7384
Variance0.72350748
MonotonicityNot monotonic
2024-08-29T00:32:25.209579image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 411
 
19.5%
1 234
 
11.1%
2 183
 
8.7%
3 75
 
3.6%
0.110174 2
 
0.1%
1.661556 2
 
0.1%
0.245354 2
 
0.1%
1.067817 2
 
0.1%
0.288032 2
 
0.1%
1.252472 2
 
0.1%
Other values (1180) 1196
56.7%
ValueCountFrequency (%)
0 411
19.5%
9.6 × 10-51
 
< 0.1%
0.000272 1
 
< 0.1%
0.000454 1
 
< 0.1%
0.001015 1
 
< 0.1%
0.001086 1
 
< 0.1%
0.001272 1
 
< 0.1%
0.001297 1
 
< 0.1%
0.00203 1
 
< 0.1%
0.00342 1
 
< 0.1%
ValueCountFrequency (%)
3 75
3.6%
2.999918 1
 
< 0.1%
2.998981 1
 
< 0.1%
2.971832 1
 
< 0.1%
2.939733 1
 
< 0.1%
2.936551 1
 
< 0.1%
2.931527 1
 
< 0.1%
2.892922 2
 
0.1%
2.891986 1
 
< 0.1%
2.89118 1
 
< 0.1%

TUE
Real number (ℝ)

ZEROS 

Distinct1129
Distinct (%)53.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.65786592
Minimum0
Maximum2
Zeros557
Zeros (%)26.4%
Negative0
Negative (%)0.0%
Memory size16.6 KiB
2024-08-29T00:32:25.410243image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.62535
Q31
95-th percentile2
Maximum2
Range2
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.60892726
Coefficient of variation (CV)0.92560997
Kurtosis-0.5486604
Mean0.65786592
Median Absolute Deviation (MAD)0.484872
Skewness0.61850241
Sum1388.755
Variance0.37079241
MonotonicityNot monotonic
2024-08-29T00:32:25.622946image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 557
26.4%
1 292
 
13.8%
2 109
 
5.2%
0.630866 4
 
0.2%
1.119877 3
 
0.1%
0.0026 3
 
0.1%
0.009254 2
 
0.1%
0.8324 2
 
0.1%
1.36595 2
 
0.1%
0.828549 2
 
0.1%
Other values (1119) 1135
53.8%
ValueCountFrequency (%)
0 557
26.4%
7.3 × 10-51
 
< 0.1%
0.000355 1
 
< 0.1%
0.000436 1
 
< 0.1%
0.001096 1
 
< 0.1%
0.00133 1
 
< 0.1%
0.001337 1
 
< 0.1%
0.001518 1
 
< 0.1%
0.00159 1
 
< 0.1%
0.00164 1
 
< 0.1%
ValueCountFrequency (%)
2 109
5.2%
1.99219 1
 
< 0.1%
1.990617 1
 
< 0.1%
1.983678 1
 
< 0.1%
1.980875 1
 
< 0.1%
1.978043 1
 
< 0.1%
1.972926 1
 
< 0.1%
1.97117 1
 
< 0.1%
1.969507 1
 
< 0.1%
1.967259 1
 
< 0.1%

CAEC
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size16.6 KiB
Sometimes
1765 
Frequently
242 
Always
 
53
no
 
51

Length

Max length10
Median length9
Mean length8.8702037
Min length2

Characters and Unicode

Total characters18725
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSometimes
2nd rowSometimes
3rd rowSometimes
4th rowSometimes
5th rowSometimes

Common Values

ValueCountFrequency (%)
Sometimes 1765
83.6%
Frequently 242
 
11.5%
Always 53
 
2.5%
no 51
 
2.4%

Length

2024-08-29T00:32:25.841671image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-29T00:32:26.008221image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
sometimes 1765
83.6%
frequently 242
 
11.5%
always 53
 
2.5%
no 51
 
2.4%

Most occurring characters

ValueCountFrequency (%)
e 4014
21.4%
m 3530
18.9%
t 2007
10.7%
s 1818
9.7%
o 1816
9.7%
S 1765
9.4%
i 1765
9.4%
y 295
 
1.6%
l 295
 
1.6%
n 293
 
1.6%
Other values (7) 1127
 
6.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18725
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 4014
21.4%
m 3530
18.9%
t 2007
10.7%
s 1818
9.7%
o 1816
9.7%
S 1765
9.4%
i 1765
9.4%
y 295
 
1.6%
l 295
 
1.6%
n 293
 
1.6%
Other values (7) 1127
 
6.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18725
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 4014
21.4%
m 3530
18.9%
t 2007
10.7%
s 1818
9.7%
o 1816
9.7%
S 1765
9.4%
i 1765
9.4%
y 295
 
1.6%
l 295
 
1.6%
n 293
 
1.6%
Other values (7) 1127
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18725
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 4014
21.4%
m 3530
18.9%
t 2007
10.7%
s 1818
9.7%
o 1816
9.7%
S 1765
9.4%
i 1765
9.4%
y 295
 
1.6%
l 295
 
1.6%
n 293
 
1.6%
Other values (7) 1127
 
6.0%

MTRANS
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size16.6 KiB
Public_Transportation
1580 
Automobile
457 
Walking
 
56
Motorbike
 
11
Bike
 
7

Length

Max length21
Median length21
Mean length18.128375
Min length4

Characters and Unicode

Total characters38269
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPublic_Transportation
2nd rowPublic_Transportation
3rd rowPublic_Transportation
4th rowWalking
5th rowPublic_Transportation

Common Values

ValueCountFrequency (%)
Public_Transportation 1580
74.8%
Automobile 457
 
21.6%
Walking 56
 
2.7%
Motorbike 11
 
0.5%
Bike 7
 
0.3%

Length

2024-08-29T00:32:26.181795image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-29T00:32:26.353362image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
public_transportation 1580
74.8%
automobile 457
 
21.6%
walking 56
 
2.7%
motorbike 11
 
0.5%
bike 7
 
0.3%

Most occurring characters

ValueCountFrequency (%)
o 4096
10.7%
i 3691
 
9.6%
t 3628
 
9.5%
a 3216
 
8.4%
n 3216
 
8.4%
r 3171
 
8.3%
l 2093
 
5.5%
b 2048
 
5.4%
u 2037
 
5.3%
P 1580
 
4.1%
Other values (13) 9493
24.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 38269
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 4096
10.7%
i 3691
 
9.6%
t 3628
 
9.5%
a 3216
 
8.4%
n 3216
 
8.4%
r 3171
 
8.3%
l 2093
 
5.5%
b 2048
 
5.4%
u 2037
 
5.3%
P 1580
 
4.1%
Other values (13) 9493
24.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 38269
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 4096
10.7%
i 3691
 
9.6%
t 3628
 
9.5%
a 3216
 
8.4%
n 3216
 
8.4%
r 3171
 
8.3%
l 2093
 
5.5%
b 2048
 
5.4%
u 2037
 
5.3%
P 1580
 
4.1%
Other values (13) 9493
24.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 38269
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 4096
10.7%
i 3691
 
9.6%
t 3628
 
9.5%
a 3216
 
8.4%
n 3216
 
8.4%
r 3171
 
8.3%
l 2093
 
5.5%
b 2048
 
5.4%
u 2037
 
5.3%
P 1580
 
4.1%
Other values (13) 9493
24.8%

NObeyesdad
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size16.6 KiB
Obesity_Type_I
351 
Obesity_Type_III
324 
Obesity_Type_II
297 
Overweight_Level_I
290 
Overweight_Level_II
290 
Other values (2)
559 

Length

Max length19
Median length16
Mean length16.192326
Min length13

Characters and Unicode

Total characters34182
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNormal_Weight
2nd rowNormal_Weight
3rd rowNormal_Weight
4th rowOverweight_Level_I
5th rowOverweight_Level_II

Common Values

ValueCountFrequency (%)
Obesity_Type_I 351
16.6%
Obesity_Type_III 324
15.3%
Obesity_Type_II 297
14.1%
Overweight_Level_I 290
13.7%
Overweight_Level_II 290
13.7%
Normal_Weight 287
13.6%
Insufficient_Weight 272
12.9%

Length

2024-08-29T00:32:26.537973image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-29T00:32:26.724591image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
obesity_type_i 351
16.6%
obesity_type_iii 324
15.3%
obesity_type_ii 297
14.1%
overweight_level_i 290
13.7%
overweight_level_ii 290
13.7%
normal_weight 287
13.6%
insufficient_weight 272
12.9%

Most occurring characters

ValueCountFrequency (%)
e 5095
14.9%
_ 3663
 
10.7%
I 3059
 
8.9%
i 2655
 
7.8%
t 2383
 
7.0%
y 1944
 
5.7%
O 1552
 
4.5%
s 1244
 
3.6%
v 1160
 
3.4%
g 1139
 
3.3%
Other values (17) 10288
30.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 34182
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 5095
14.9%
_ 3663
 
10.7%
I 3059
 
8.9%
i 2655
 
7.8%
t 2383
 
7.0%
y 1944
 
5.7%
O 1552
 
4.5%
s 1244
 
3.6%
v 1160
 
3.4%
g 1139
 
3.3%
Other values (17) 10288
30.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 34182
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 5095
14.9%
_ 3663
 
10.7%
I 3059
 
8.9%
i 2655
 
7.8%
t 2383
 
7.0%
y 1944
 
5.7%
O 1552
 
4.5%
s 1244
 
3.6%
v 1160
 
3.4%
g 1139
 
3.3%
Other values (17) 10288
30.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 34182
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 5095
14.9%
_ 3663
 
10.7%
I 3059
 
8.9%
i 2655
 
7.8%
t 2383
 
7.0%
y 1944
 
5.7%
O 1552
 
4.5%
s 1244
 
3.6%
v 1160
 
3.4%
g 1139
 
3.3%
Other values (17) 10288
30.1%

Interactions

2024-08-29T00:32:19.157558image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-29T00:32:10.798907image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-29T00:32:12.032991image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-29T00:32:13.187811image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-29T00:32:14.340623image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-29T00:32:15.505477image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-29T00:32:16.672337image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-29T00:32:18.025814image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-29T00:32:19.291001image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-29T00:32:10.971477image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-29T00:32:12.156397image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-29T00:32:13.312221image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-29T00:32:14.470051image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-29T00:32:15.632898image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-29T00:32:16.804777image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-29T00:32:18.151229image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-29T00:32:19.432470image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-29T00:32:11.197227image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-29T00:32:12.286829image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-29T00:32:13.451683image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-29T00:32:14.614529image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-29T00:32:15.765336image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-29T00:32:16.946243image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-29T00:32:18.283667image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-29T00:32:19.578952image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-29T00:32:11.328661image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-29T00:32:12.423280image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-29T00:32:13.588133image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-29T00:32:14.755997image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-29T00:32:15.907808image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-29T00:32:17.096740image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-29T00:32:18.421123image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-29T00:32:19.735471image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-29T00:32:11.473138image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-29T00:32:12.575787image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-29T00:32:13.757696image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-29T00:32:14.911511image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-29T00:32:16.055297image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-29T00:32:17.250250image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-29T00:32:18.573629image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-29T00:32:19.878944image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-29T00:32:11.609589image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-29T00:32:12.709226image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-29T00:32:13.894148image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-29T00:32:15.059002image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-29T00:32:16.240911image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-29T00:32:17.397737image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-29T00:32:18.713089image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-29T00:32:20.033455image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-29T00:32:11.758080image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-29T00:32:12.861730image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-29T00:32:14.051668image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-29T00:32:15.207490image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-29T00:32:16.387396image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-29T00:32:17.557266image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-29T00:32:18.863587image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-29T00:32:20.178939image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-29T00:32:11.884498image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-29T00:32:13.039318image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-29T00:32:14.186111image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-29T00:32:15.350967image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-29T00:32:16.524849image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-29T00:32:17.867291image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-29T00:32:19.000037image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-08-29T00:32:26.902179image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
AgeCAECCALCCH2OFAFFAVCFCVCGenderHeightMTRANSNCPNObeyesdadSCCSMOKETUEWeightfamily_history_with_overweight
Age1.0000.1570.1630.013-0.2080.1380.0620.196-0.0030.350-0.1060.2960.1410.178-0.2980.3570.239
CAEC0.1571.0000.0980.1830.1170.1930.1300.1310.1510.0950.1670.3520.1600.0460.1330.3180.349
CALC0.1630.0981.0000.1070.1110.1370.0990.0330.0980.0950.1210.2250.0550.1040.1380.2190.012
CH2O0.0130.1830.1071.0000.1560.1950.0660.2380.2250.0900.0700.2350.1310.0750.0230.2260.233
FAF-0.2080.1170.1110.1561.0000.1560.0280.2650.3260.1150.1450.2120.1000.0680.051-0.0440.159
FAVC0.1380.1930.1370.1950.1561.0000.0880.0600.2120.2010.0420.3280.1860.0400.1710.2930.205
FCVC0.0620.1300.0990.0660.0280.0881.0000.347-0.0560.1050.0860.2930.0940.000-0.0880.2080.121
Gender0.1960.1310.0330.2380.2650.0600.3471.0000.6160.1620.1620.5560.0980.0350.1310.3960.099
Height-0.0030.1510.0980.2250.3260.212-0.0560.6161.0000.0860.2040.2040.1740.1770.0820.4630.293
MTRANS0.3500.0950.0950.0900.1150.2010.1050.1620.0861.0000.0400.1790.0700.0000.1260.1400.118
NCP-0.1060.1670.1210.0700.1450.0420.0860.1620.2040.0401.0000.2450.0450.0280.0870.0030.190
NObeyesdad0.2960.3520.2250.2350.2120.3280.2930.5560.2040.1790.2451.0000.2350.1110.2170.5750.540
SCC0.1410.1600.0550.1310.1000.1860.0940.0980.1740.0700.0450.2351.0000.0330.1290.2350.181
SMOKE0.1780.0460.1040.0750.0680.0400.0000.0350.1770.0000.0280.1110.0331.0000.0580.1290.000
TUE-0.2980.1330.1380.0230.0510.171-0.0880.1310.0820.1260.0870.2170.1290.0581.000-0.0500.188
Weight0.3570.3180.2190.226-0.0440.2930.2080.3960.4630.1400.0030.5750.2350.129-0.0501.0000.557
family_history_with_overweight0.2390.3490.0120.2330.1590.2050.1210.0990.2930.1180.1900.5400.1810.0000.1880.5571.000

Missing values

2024-08-29T00:32:20.396657image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-08-29T00:32:20.747819image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

AgeGenderHeightWeightCALCFAVCFCVCNCPSCCSMOKECH2Ofamily_history_with_overweightFAFTUECAECMTRANSNObeyesdad
021.0Female1.6264.0nono2.03.0nono2.0yes0.01.0SometimesPublic_TransportationNormal_Weight
121.0Female1.5256.0Sometimesno3.03.0yesyes3.0yes3.00.0SometimesPublic_TransportationNormal_Weight
223.0Male1.8077.0Frequentlyno2.03.0nono2.0yes2.01.0SometimesPublic_TransportationNormal_Weight
327.0Male1.8087.0Frequentlyno3.03.0nono2.0no2.00.0SometimesWalkingOverweight_Level_I
422.0Male1.7889.8Sometimesno2.01.0nono2.0no0.00.0SometimesPublic_TransportationOverweight_Level_II
529.0Male1.6253.0Sometimesyes2.03.0nono2.0no0.00.0SometimesAutomobileNormal_Weight
623.0Female1.5055.0Sometimesyes3.03.0nono2.0yes1.00.0SometimesMotorbikeNormal_Weight
722.0Male1.6453.0Sometimesno2.03.0nono2.0no3.00.0SometimesPublic_TransportationNormal_Weight
824.0Male1.7864.0Frequentlyyes3.03.0nono2.0yes1.01.0SometimesPublic_TransportationNormal_Weight
922.0Male1.7268.0noyes2.03.0nono2.0yes1.01.0SometimesPublic_TransportationNormal_Weight
AgeGenderHeightWeightCALCFAVCFCVCNCPSCCSMOKECH2Ofamily_history_with_overweightFAFTUECAECMTRANSNObeyesdad
210125.722004Female1.628470107.218949Sometimesyes3.03.0nono2.487070yes0.0673290.455823SometimesPublic_TransportationObesity_Type_III
210225.765628Female1.627839108.107360Sometimesyes3.03.0nono2.320068yes0.0452460.413106SometimesPublic_TransportationObesity_Type_III
210321.016849Female1.724268133.033523Sometimesyes3.03.0nono1.650612yes1.5376390.912457SometimesPublic_TransportationObesity_Type_III
210421.682367Female1.732383133.043941Sometimesyes3.03.0nono1.610768yes1.5103980.931455SometimesPublic_TransportationObesity_Type_III
210521.285965Female1.726920131.335786Sometimesyes3.03.0nono1.796267yes1.7283320.897924SometimesPublic_TransportationObesity_Type_III
210620.976842Female1.710730131.408528Sometimesyes3.03.0nono1.728139yes1.6762690.906247SometimesPublic_TransportationObesity_Type_III
210721.982942Female1.748584133.742943Sometimesyes3.03.0nono2.005130yes1.3413900.599270SometimesPublic_TransportationObesity_Type_III
210822.524036Female1.752206133.689352Sometimesyes3.03.0nono2.054193yes1.4142090.646288SometimesPublic_TransportationObesity_Type_III
210924.361936Female1.739450133.346641Sometimesyes3.03.0nono2.852339yes1.1391070.586035SometimesPublic_TransportationObesity_Type_III
211023.664709Female1.738836133.472641Sometimesyes3.03.0nono2.863513yes1.0264520.714137SometimesPublic_TransportationObesity_Type_III

Duplicate rows

Most frequently occurring

AgeGenderHeightWeightCALCFAVCFCVCNCPSCCSMOKECH2Ofamily_history_with_overweightFAFTUECAECMTRANSNObeyesdad# duplicates
521.0Male1.6270.0Sometimesyes2.01.0nono3.0no1.00.0noPublic_TransportationOverweight_Level_I15
421.0Female1.5242.0Sometimesyes3.01.0nono1.0no0.00.0FrequentlyPublic_TransportationInsufficient_Weight4
016.0Female1.6658.0nono2.01.0nono1.0no0.01.0SometimesWalkingNormal_Weight2
118.0Female1.6255.0noyes2.03.0nono1.0yes1.01.0FrequentlyPublic_TransportationNormal_Weight2
218.0Male1.7253.0Sometimesyes2.03.0nono2.0yes0.02.0SometimesPublic_TransportationInsufficient_Weight2
321.0Female1.5242.0Sometimesno3.01.0nono1.0no0.00.0FrequentlyPublic_TransportationInsufficient_Weight2
622.0Female1.6965.0Sometimesyes2.03.0nono2.0yes1.01.0SometimesPublic_TransportationNormal_Weight2
722.0Male1.7475.0noyes3.03.0nono1.0yes1.00.0FrequentlyAutomobileNormal_Weight2
825.0Female1.5755.0Sometimesyes2.01.0nono2.0no2.00.0SometimesPublic_TransportationNormal_Weight2